Exploratory Data Analysis with Pandas Python 2023

120K subscribers

259K views 1 year ago Medallion Python Data Science Coding Videos

In this video about exploratory data analysis with pandas and python, Kaggle grandmaster Rob Mulla will teach you the basics of how to explore data using python and pandas. Exploratory Data Analysis it a necessary tool for any data scientist. Pandas is a MUST for anyone getting into data science with python. Python is the #1 coding language for data science and has been growing over the years as an essential tool, with Pandas being the main data wrangling

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Pinned by Rob Mulla

Chapters don't appear to be working for my videos for some reason. Here are the timestamps for the video: 00:00 Introduction 01:00 Imports and reading data 03:35 Data Understanding 06:40 Data Preparation 20:57 Feature Understanding 27:35 Feature Relationships 35:30 Asking a Question about the Data 40:00 Final Thoughts

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@saintsaens3517

As a begginer in data this really opend my eyes as to how things works. Your explanations are very clear and I can feel how passionate you are. Great video

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@saptarshidey7672

There are a ton of EDA videos on YouTube. This is one of the best I have ever come across. You just nailed it, Rob.

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@sa-pt3kf

By far one of the most clear and concise ways of teaching in a computer science related field I've come across in a while. I'll be binging all your tutorials for sure!

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@Aarron-io3pm

5 months ago (edited)

Hi Rob, this was super useful to me as a tired Excel veteran and python beginner. You explain and demonstrate everything so clearly, thank you

@JHornsby89

This is the second one of these I have now watched and coded along with! Genuinely awesome content, so precise and simple to follow. You make daunting tasks (for beginners getting into data) really accessible which is a sign of a great teacher!

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@jackgarn8392

This is such an amazing guide! I’m new to data analysis and had limited python exposure and have taught myself most of these things so far by googling or just reading the pandas documentation. Watching someone familiar with the process do it all together was really helpful and gave me a lot of insights as to how I can improve my skills and workflow. Thank you so so much!

@chrisosomo2856

I can’t get enough of your videos, especially the very hands-on practical approach to learning. Your explanations are clear and easy to follow along with. Please make more of these types of videos. You are definitely makes a change and contributing to the YouTube knowledge pool. Thank you so much.

@SearchingforScraps

Wow ! this is such a clean run through. You make it look so easy and easy to learn ! Thank you so much. This is giving me the confidence to finally start something on my own.

@wesleyweel8007

The quality of your content is only surpassed by the ease at which it is to assimilate it, keep up the great content Rob, cheers!

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@romanrodin5669

Great channel! Very helpful for beginners and for those who' re digging deeper and moving forward into DS industry as myself! Thanks Rob!

@silver_soul98

I have tried plenty of tutorials by now. This is the most precise and to-the-point tutorial so far. Well done.

@leonrobinson2053

Late to the party but this is really really good. Helps you dig in to the detail (rather than you thinking, how do I do what I'm thinking I need to do). This should be a template to use as it general enough for you to pick it up but specific enough with examples to be used elsewhere

@dhauss8218

This is incredible, I have been out of school for a few years and I'm now super inspired to learn python/pandas and the other libraries. One thing I wanted to point out, maybe I'm wrong, but would the solution for the sample question involve looking at the upper quartile of the roller coaster speeds as opposed to averages? This way locations with more coasters in total, would not be averaged down by slower coasters in their total count?

@rajeevnair8815

Thanks for the excellent video on EDA, Rob. It'd be great if you could do a complete EDA on a relatively complex dataset, wherein we have scope for feature engineering, by exploring feature interactions and stuff like that. Thanks!

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@aayaanhasnain5143

Hey Rob, really admired the way you explained complicated topics with ease!! Looking forward to learning from you more :)

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@mattsmithyy

Great video, really informative! One question - when dropping the duplicate rows would it not be better to use the .drop_duplicates method? This way you can determine whether to keep the first or last duplicate whereas here it is unclear which row will be kept.

@muhammadfadliaktsar7172

Thank you Rob for your explanation, before this it was hard for me to study and my mind just start pressured me of how to do EDA with Python language. And this video just open my mind to study it!

@jonathanhody3622

Extremely interesting! Thank you for the content, I really like the way you go step by step in your approach, it makes it very easy to understand for non-advanced people like me

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@marcinkrol7984

Thanks - very interesting video. I am only hesitating if it makes sense to use dropna() when using corr() on a DataFrame (as you did at 34:01). My impression is that pandas does it itself for every pair of features (it means it drops rows when there are missing values in this pair), so adding dropna() as you did only limits number of observations (rows) that are under analysis of correlation. Am I wrong?

@sheshankjoshi

This is perfect for my interview tomorrow. I just needed a refresher on how to approach the problem, ask right questions and then come up with exploratory options. Thank You so much for this video

@user-et7zv5rs3q

this is absolutely amazing! Follow your video step by step actually make me more confident of my coding!

@wahabamin6946

Thanks Rob, you’re doing a great job for the data science community. Your videos here and on TikTok is helping me a lot in this journey. Thank you.

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@iiN1GH7M4R3ii

Great stuff man, thanks so much for this. Youre great at teaching beginners!!!!

@moxie1189

Hey Rob, amazing video. Would you consider an A/B testing project?

@rrestituti

#1 Data science youtuber!!! You made easy to understand the basic commands e sintaxes. Thank you a lot, Rob.

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@siddhant0701

This was a really nice tutorial, Rob. Had fun coding along, thanks for doing it :)

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@eyadamin4089

Hello , I hope you can make a video about statistics , explain what ever plot is telling you and how will each plot help you and stuff like that , thank you very much btw very clear and clean explanation

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@walterpark8824

Terrific introductory survey that answered so many of my questions, moving from SQL. Looks extremely efficient. Now, to plug into my data! Thanks.

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@pythonfordataanalysis84

You are the best coach. Thank you, sir!

@nunolopes3910

This type of videos are amazing to follow, i am starting to use python for data analysis and i could not happier! Your channel is helping me alot, thank you!

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@shihaosun6861

Thank you very much Rob for this wonderful walkthrough and explanation! Really Appreciate it!!!!

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@nguyenduyta7136

Many thanks for your good working. You have a good voice to hear and good words to understand

. Hope one day you open a data science course from zero to hero that help people have good jobs

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@codecode6419

I am spending my weekend with your videos and I would like to say that I have learned several tricks to use python's libraries efficiently. Thanks for your explanation and your time to provide the videos

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@ziggle314

Excellent example, Rob. Thanks for these great tutorials.

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@rdatta

Excellent work and introduction. Very well done!

@anuragarunedlabadkar8889

Excellent explanation Rob. Learned a lot from this video. Keep it up.

@lashlarue7924

Rob you are the MAN! Thank you for sharing your knowledge with the rest of us!

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@adarshtiwari7395

I have been grinding through your videos lately in preperation for my data science job and you have been an absolute blessing! Thanks a bunch!!

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@alisonhenley2551

Wow, what an informative fun tutorial. Thanks Rob!

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These chapters are auto-generated

Intro

Kaggle Notebook

Data Preparation

Drop Command

DTypes

Rename

Missing Values

Duplicated

Value Counts

Plot

Histogram

Feature Relationships

Scatter Plot

Pair Plot

Correlation

Question

Linnea Caraballo

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Exploratory Data Analysis with Pandas Python 2023

Rob Mulla

In this video about exploratory data analysis with pandas and python, Kaggle grandmaster Rob Mulla will teach you the basics of how to explore data using python and pandas. Exploratory Data Analysis it a necessary tool for any data scientist. Pandas is a MUST for anyone getting into data science with python. Python is the #1 coding language for data science and has been growing over the years as an essential tool, with Pandas being the main data wrangling module. Kaggle Grandmaster Rob goes over it all in this video. In this video we discuss the basics of how to use explore data including... Timestamps: 00:00 Introduction 01:00 Imports and reading data 03:35 Data Understanding 06:40 Data Preparation 20:57 Feature Understanding 27:35 Feature Relationships 35:30 Asking a Question about the Data 40:00 Final Thoughts Follow me on twitch for live coding streams: https://www.twitch.tv/medallionstallion_ Intro to Pandas video: • A Gentle Introduction to Pandas Data ... Link to kaggle notebook used in the tutorial: https://www.kaggle.com/robikscube/int... * Youtube: / @robmulla * Twitch: https://www.twitch.tv/medallionstallion_ * Twitter: https://twitter.com/MedallionData * Kaggle: https://www.kaggle.com/robikscube #Python #Coding #DataScience #Kaggle

Intro

Kaggle Notebook

Data Preparation

Drop Command

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Rob Mulla

120K subscribers

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